GNSS interference source optimal layout method based on genetic algorithm

Abstract

The invention discloses a global navigation satellite system (GNSS) interference source optimal layout method based on a genetic algorithm. The GNSS interference source optimal layout method comprises the following steps: step 1, initializing a population; step 2, calculating interference intensity; step 3, selecting; according to the method disclosed by the invention, the optimal layout algorithm is combined with the radio wave propagation model, the optimal interference source layout scheme can be calculated in different environments, the interference coverage area and the interference intensity can be maximized, the interference effect is enhanced not only for a single interference source, but also through multi-source cooperative interference, and the method is suitable for large-scale popularization and application. And the interference signal can be ensured to cover a blind area of the target area and an area with weak signals. The method provided by the invention provides technical support for reasonable arrangement of the GNSS interference source, and has important economic and military significance.

Description

Technical Field

[0001] The present invention belongs to the field of satellite navigation countermeasures, and in particular relates to an optimal placement method of GNSS interference sources based on a genetic algorithm in this field. Background Art

[0002] With the widespread adoption of global navigation satellite systems (GNSS) in critical sectors such as military, aviation, transportation, and communications, the need to protect these systems from interference has become increasingly pressing. However, in certain specialized scenarios, intentionally jamming GNSS signals to achieve strategic objectives has become necessary. Maximizing the effectiveness of jamming in these scenarios and achieving optimal jamming deployment have become crucial issues.

[0003] GNSS systems rely on radio signals transmitted from satellites to the ground for positioning and time synchronization. These signals are very fragile and easily affected by the surrounding environment. Under normal circumstances, GNSS signals are transmitted through relatively stable radio wave propagation paths in space. However, once they are subject to external electromagnetic interference (such as intentional signal blocking or interference sources), the quality and accuracy of the signals will be greatly reduced, and even the signal will be completely lost. In special fields, the goal of maximizing the interference effect is to precisely deploy interference sources so that they can weaken or block the signal transmission of the GNSS system to the greatest extent in a specific area, thereby affecting the enemy's positioning and navigation capabilities. Therefore, there is an urgent need for an efficient and scientific optimization method to improve the effect and accuracy of interference source deployment. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an optimal layout method of GNSS interference sources based on genetic algorithm.

[0005] The present invention adopts the following technical solutions:

[0006] A method for optimal placement of GNSS interference sources based on a genetic algorithm is improved in that it includes the following steps:

[0007] Step 1, initialize the population:

[0008] Generate several interference source layout plans, each plan corresponds to a chromosome population, and each gene on the chromosome represents the position coordinates of each interference source;

[0009] Step 2: Calculate the interference intensity:

[0010] Using the radio wave propagation model and the transmission power of the interference source, the interference intensity of each interference source layout plan in a specific area is calculated;

[0011] Step 3. Select an action:

[0012] According to the interference intensity, excellent chromosomes are selected as parents for reproduction. The selection operation adopts the roulette wheel selection method, that is, the selection probability of each chromosome is proportional to its total interference intensity. The higher the total interference intensity, the greater the probability of chromosomes being selected.

[0013] Step 4, cross operation:

[0014] Select a crossover point and perform a crossover operation on the parent chromosome to combine the genes with high interference intensity in the parent to generate a new daughter chromosome;

[0015] Step 5, mutation operation:

[0016] Perform mutation operations on the daughter chromosomes. Each chromosome contains the position coordinates of multiple interference sources. Position mutation is achieved by randomly adjusting the coordinates of a certain interference source.

[0017] Step 6, iterative optimization:

[0018] Repeat the selection, crossover, and mutation operations to generate a new population, and optimize the layout of the interference source generation by generation until the preset stopping condition is met and the optimal location of the interference source is determined.

[0019] Furthermore, the step 1 specifically includes:

[0020] Chromosome code:

[0021] Each chromosome population corresponds to an interference source layout scheme, and each gene in the chromosome represents the specific location of an interference source. For the layout of interference sources in two-dimensional space, the chromosome gene uses x, y coordinates to represent the location of the interference source;

[0022] Generate population:

[0023] A set of initial populations is randomly generated, the size of the population is preset by the user, and the gene value of each chromosome is randomly initialized according to the given region and layout requirements.

[0024] Furthermore, in step 2:

[0025] The interference intensity I is calculated using the following formula:

[0026]

[0027] In the above formula, P t is the transmit power of the interference source, L path_loss It is the path loss calculated using the Hata model of radio wave propagation.

[0028] Furthermore, the step 3 specifically includes:

[0029] Calculate the interference intensity of all chromosomes;

[0030] Calculate the selection probability of each chromosome, which is the ratio of the interference intensity of the chromosome to the total interference intensity;

[0031] These selection probabilities are mapped to a roulette wheel, and the selected chromosome is determined by randomly generating a value during selection.

[0032] Furthermore, the step 4 specifically includes:

[0033] Select parent chromosomes: select two chromosomes from the population as parents;

[0034] Select crossover points: Select multiple crossover points in the gene sequence of the parent chromosome, exchange multiple parts of the parent chromosome, and generate new daughter chromosomes.

[0035] Exchange genes: The genes of the parent chromosome are exchanged according to the crossover point to produce two daughter chromosomes.

[0036] The beneficial effects of the present invention are:

[0037] The method disclosed in this paper combines an optimal placement algorithm with a radio wave propagation model to calculate the optimal interference source placement scheme under different environments, maximizing the interference coverage area and interference intensity. This not only targets a single interference source, but also enhances the interference effect through multi-source coordinated interference, ensuring that the interference signal can cover blind spots and weak signal areas in the target area. This method provides technical support for the rational placement of GNSS interference sources and has important economic and military significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0039] Figure 2 This is an example of the layout of interference to a single receiver;

[0040] Figure 3 This is an example of the layout of interference to three receivers. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] Genetic algorithms (GAs), as powerful global optimization algorithms, have been widely used in combinatorial optimization problems due to their ability to find optimal solutions in complex solution spaces. By combining GAs with radio wave propagation models to optimally place GNSS interference sources, GAs can more accurately assess signal propagation characteristics, precisely predict the interference effects at different locations, and reduce the potential for blind placement of interference sources, ultimately designing an optimal interference source placement plan.

[0043] Example 1: This example discloses a method for optimal placement of GNSS interference sources based on a genetic algorithm. Figure 1 As shown, the following steps are included:

[0044] Step 1, initialize the population:

[0045] Generate several interference source layout plans, each plan corresponds to a chromosome population, and each gene on the chromosome represents the position coordinates of each interference source;

[0046] In a genetic algorithm, the first step is to initialize the population. A population is a set of potential solutions, each consisting of several individuals (chromosomes). Each individual represents a specific interference source placement scheme, typically using chromosome encoding to represent the location of the interference source.

[0047] Chromosome code:

[0048] Each chromosome population consists of multiple genes, each of which represents the location coordinates of an interference source. For the layout of interference sources in two-dimensional space, the genes of the chromosome can use (x, y) coordinates to represent the location of the interference source. Each chromosome corresponds to an interference source layout scheme, and each gene in the chromosome represents the specific location of an interference source;

[0049] Generate population:

[0050] A set of initial populations is randomly generated, the size of the population is preset by the user, and the gene value (coordinates of the interference source) of each individual (chromosome) is randomly initialized according to the given area and layout requirements.

[0051] For example, if the target area is a rectangular area, a certain number of interference sources can be randomly distributed in this area to ensure that the population contains multiple possible deployment schemes.

[0052] Step 2: Calculate the interference intensity:

[0053] Using a radio wave propagation model and the interference source's transmit power, we calculate the interference intensity for each interference source deployment scenario within a specific area. Interference intensity is typically determined by factors such as the source's power, distance, and propagation environment. The greater the interference intensity, the greater the impact on GNSS receivers within the target area. Therefore, the goal is to maximize interference intensity.

[0054] The interference intensity I is calculated using the following formula:

[0055]

[0056] In the above formula, P t is the transmit power of the interference source (in watts), L path_loss It is the path loss calculated using the Hata model of radio wave propagation and is expressed in decibels (dB).

[0057] Interference intensity is usually expressed in decibel milliwatts (dBm), and the conversion formula is:

[0058]

[0059] The goal is to maximize the total interference intensity in the target area, that is, the sum of the interference intensities of all interference sources on the target area is maximized.

[0060] For urban environments, the path loss of the Hata model (L urban ) can be expressed by the following formula:

[0061] L urban (dB)=69.55+26.16log 10 (f)-13.82log 10 (h t )-a(h r )

[0062] +[44.9-6.55log 10 (h t )]log 10 (d)

[0063] In the above formula, f is the frequency of the signal (MHz), h t is the transmitter antenna height (m), h r is the height of the receiver antenna (m), d is the distance between the transmitter and the receiver (km), a(h r ) is a correction term related to the receiver altitude, and the calculation formula is:

[0064] a(h r )=(1.1log 10 (f)-0.7)h r -(1.56log 10 (f)-0.8)

[0065] For suburban environments, the path loss of the Hata model (L suburban ) is expressed as:

[0066] Lsuburban (dB) = L urban (dB)-2[log 10 (f / 28)] 2 -5.4

[0067] The significance of this formula is that in a suburban environment, the propagation loss is smaller than that in an urban environment.

[0068] For open area environment, the path loss of Hata model (L rural ) is expressed as:

[0069] L rural (dB) = L urban (dB)-4.78[log 10 (f)] 2 +18.33log 10 (f)-40.94

[0070] In open areas, propagation losses are usually smaller, mainly because the influence of buildings is smaller.

[0071] The interference intensity I is calculated based on the path loss, that is, the intensity of the interference signal decreases as the distance increases during the propagation process.

[0072] Step 3. Select an action:

[0073] According to the interference intensity, excellent chromosomes are selected as parents for reproduction. The selection operation adopts the roulette wheel selection method, that is, the selection probability of each chromosome is proportional to its total interference intensity. The higher the total interference intensity, the greater the probability of chromosomes being selected.

[0074] The purpose of the selection operation is to select chromosomes with higher interference intensity from the current population to generate the next generation population. The probability of selection is based on the magnitude of the interference intensity. The higher the interference intensity, the greater the probability of selection. This is suitable for situations where the interference intensity is maximized. The specific process is as follows:

[0075] Calculate the interference intensity of all chromosomes;

[0076] Calculate the selection probability of each chromosome, which is the ratio of the interference intensity of the chromosome to the total interference intensity;

[0077] These selection probabilities are mapped to a roulette wheel, and the selected chromosome is determined by randomly generating a value during selection.

[0078] Step 4, cross operation:

[0079] Select a crossover point and perform a crossover operation on the parent chromosome to combine genes with high interference intensity in the parent chromosome to generate a new daughter chromosome. The crossover operation explores the location of new interference sources by exchanging part of the genes in the parent chromosome.

[0080] The crossover operation simulates the process of genetic recombination, generating a daughter chromosome by crossing two parent chromosomes. The goal of the crossover operation is to generate new solutions by combining the genes of the parent chromosomes, thereby accelerating the search process. Here, the crossover operation enriches the richness of the interference source space search. The general process of the crossover operation is as follows:

[0081] Select parent chromosomes: select two chromosomes from the population as parents (usually done through a selection operation);

[0082] Select crossover points: Select multiple crossover points in the gene sequence of the parent chromosome and exchange multiple parts of the parent chromosome to produce new daughter chromosomes. This method increases the mixing degree of genes and sometimes can produce a richer optimal space search;

[0083] Exchange genes: The genes of the parent chromosome are exchanged according to the crossover point to produce two daughter chromosomes.

[0084] Step 5, mutation operation:

[0085] Perform mutation operations in offspring chromosomes to prevent the algorithm from falling into local optimal solutions;

[0086] Each chromosome contains the position coordinates of multiple interference sources, and position variation is achieved by randomly adjusting the coordinates of a certain interference source (for example, randomly moving the interference source position within a certain range);

[0087] The purpose of mutation is to increase the diversity of the population and prevent the algorithm from falling into a local optimal solution. New chromosomes are generated by making small random changes to the genes of the chromosome. Mutation simulates the process of gene mutation. The mutation process usually includes:

[0088] Select a chromosome: Select a chromosome from the population.

[0089] Select mutation position: select a random position (or multiple positions) in the gene of the chromosome and then change the gene value at that position.

[0090] Mutation: Randomly transforming a gene according to its encoding. For example, in binary encoding, mutation means changing a 0 to a 1 or a 1 to a 0.

[0091] Step 6, iterative optimization:

[0092] Repeat the selection, crossover, mutation and other operations to generate a new population, optimize the layout of the interference source generation by generation until the preset stopping condition is met and the optimal location of the interference source is determined. Figure 2 This is an example of the layout of interference to a single receiver; Figure 3 This is an example of the layout of interference to three receivers.

Claims

1. A method for optimal placement of GNSS interference sources based on genetic algorithm, characterized in that: The steps include: Step 1, initialize the population: Generate several interference source layout plans, each plan corresponds to a chromosome population, and each gene on the chromosome represents the position coordinates of each interference source; Step 2: Calculate the interference intensity: Using the radio wave propagation model and the transmission power of the interference source, the interference intensity of each interference source layout plan in a specific area is calculated; Step 3. Select an action: According to the interference intensity, excellent chromosomes are selected as parents for reproduction. The selection operation adopts the roulette wheel selection method, that is, the selection probability of each chromosome is proportional to its total interference intensity. The higher the total interference intensity, the greater the probability of chromosomes being selected. Step 4, cross operation: Select a crossover point and perform a crossover operation on the parent chromosome to combine the genes with high interference intensity in the parent to generate a new daughter chromosome; Step 5, mutation operation: Perform mutation operations on the daughter chromosomes. Each chromosome contains the position coordinates of multiple interference sources. Position mutation is achieved by randomly adjusting the coordinates of a certain interference source. Step 6, iterative optimization: Repeat the selection, crossover, and mutation operations to generate a new population, and optimize the layout of the interference source generation by generation until the preset stopping condition is met and the optimal location of the interference source is determined.

2. The optimal placement method of GNSS interference sources based on genetic algorithm according to claim 1, characterized in that: The step 1 specifically includes: Chromosome code: Each chromosome population corresponds to an interference source layout scheme, and each gene in the chromosome represents the specific location of an interference source. For the layout of interference sources in two-dimensional space, the chromosome gene uses x, y coordinates to represent the location of the interference source; Generate population: A set of initial populations is randomly generated, the size of the population is preset by the user, and the gene value of each chromosome is randomly initialized according to the given region and layout requirements.

3. The optimal placement method of GNSS interference sources based on genetic algorithm according to claim 1, characterized in that: In step 2: The interference intensity I is calculated using the following formula: In the above formula, P t is the transmit power of the interference source, L path_loss It is the path loss calculated using the Hata model of radio wave propagation.

4. The optimal placement method of GNSS interference sources based on genetic algorithm according to claim 1, characterized in that: The step 3 specifically includes: Calculate the interference intensity of all chromosomes; Calculate the selection probability of each chromosome, which is the ratio of the interference intensity of the chromosome to the total interference intensity; These selection probabilities are mapped to a roulette wheel, and the selected chromosome is determined by randomly generating a value during selection.

5. The optimal placement method of GNSS interference sources based on genetic algorithm according to claim 1, characterized in that: The step 4 specifically includes: Select parent chromosomes: select two chromosomes from the population as parents; Select crossover points: Select multiple crossover points in the gene sequence of the parent chromosome, exchange multiple parts of the parent chromosome, and generate new daughter chromosomes. Exchange genes: The genes of the parent chromosome are exchanged according to the crossover point to produce two daughter chromosomes.

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